Group Imbalance

Group imbalance, the uneven distribution of data points across different subgroups within a dataset (e.g., demographic groups), poses a significant challenge to machine learning, leading to biased and unfair models. Current research focuses on mitigating this imbalance through various techniques, including data augmentation (e.g., using generative models or resampling methods), modified loss functions that weigh classes differently, and the development of robust model architectures like graph neural networks and transformers that are less susceptible to bias. Addressing group imbalance is crucial for ensuring fairness and reliability in machine learning applications across diverse fields, from healthcare and finance to social sciences.